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    <head><title>Schelling Space</title></head>
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                    <h2>Schelling Space</h2>
                    Contact: <a href="mailto:swise5@gmu.edu">Sarah Wise</a>, <a href="mailto:mcoletti@gmu.edu">Mark Coletti</a>, or <a href="mailto:crooks2@gmu.edu">Andrew Crooks</a>,
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        <p>
        This model in a sense extends the Schelling Polygon model, however,
        instead of the polygon being the agent we take attribute data from the
        polygon model and create individual agents (see Crooks, 2010). This is
        based on the notion that much of the data we have comes at an aggregate
        level and often in some sort of vector representation of space such as
        census data. However, if we want to model the individuals or groups of
        individuals, we need to disaggregate the data.
        <p>
        To do this we create a number of Red and Blue agents based on population
        counts held within the polygon shapefile. As with the previous model, all
        agents want to be located in neighborhoods were a certain percentage of
        their neighbors are of the same type. However, instead of using a Moore
        or Von Neumann which is common practice in cell based models. Here
        neighborhoods are calculated using buffer distance from the agent in question.
        If an agent is dissatisfied with its current neighborhood, it will move
        to a random location, regardless of whether or not this new location meets
        its preference. Moreover, the model demonstrates how to link points (agents)
        to polygons along with some other basic geographical operations (such as
        union, point in polygon, buffer).
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